Harun Gokce | Mechanical | Best Mechanical Engineering Award

Published on by Engineering Scientist

Assoc. Prof. Dr. Harun Gokce | Mechanical
| Best Mechanical Engineering Award

Gazi University | Turkey

Assoc. Prof. Dr. Harun Gokce Research activities focus on advanced structural and mechanical system design, optimization, and virtual manufacturing, integrating computer-aided engineering, experimental mechanics, and intelligent simulation techniques. Work emphasizes the development of 3D simulation environments for CNC machine tools, virtual machining, and automated process optimization to improve manufacturing accuracy, efficiency, and cost performance. Significant contributions have been made to additive manufacturing, including the design of bio-inspired microstructures and bone scaffolds, enabling improved biomechanical performance in tissue engineering applications. Research also addresses multi-objective optimization of mechanical components such as gearboxes, spur gears, hydrostatic thrust bearings, and diffusers through advanced algorithms including Taguchi methods and grey wolf optimization. Additional studies involve the numerical and experimental investigation of cutting forces, thermal behavior, and tool geometries in high-precision machining processes, contributing to enhanced surface quality and tool life. Expertise in CAD/CAE platforms supports integrated modeling, analysis, and validation of complex assemblies for aerospace, automotive, and defense applications, including guided systems, aerodynamic components, and structural platforms. By combining simulation, reverse engineering, rapid prototyping, and optimization methodologies, this body of work advances smart manufacturing, lightweight design, and digitally driven engineering solutions for high-performance and mission-critical systems.

 Profile: Google Scholar

Featured Publications

Top, N., Şahin, İ., & Gökçe, H. (2021). Computer-aided design and additive manufacturing of bone scaffolds for tissue engineering: State of the art. Journal of Materials Research, 36(1), 3725–3745.

Dörterler, M., Şahin, İ., & Gökçe, H. (2018). A grey wolf optimizer approach for optimal weight design problem of the spur gear. Engineering Optimization, 51(1), 1–15.

Yavuz, M., Gökçe, H., Çiftci, I., Yavaş, C., & Şeker, U. (2020). Investigation of the effects of drill geometry on drilling performance and hole quality. International Journal of Advanced Manufacturing Technology, 106(1), 4623–4633.

Top, N., Şahin, İ., & Gökçe, H. (2023). The mechanical properties of functionally graded lattice structures derived using computer-aided design for additive manufacturing. Applied Sciences, 13(21), 1–21

Haifeng Zhai | Mechanical Engineering | Editorial Board Member

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Dr. Haifeng Zhai | Mechanical Engineering
| Editorial Board Member

Dalian University of Technology | China

Dr. Haifeng Zhai the research focuses on advancing the fundamental and applied understanding of the mechanical behavior of metallic materials, with emphasis on fatigue life prediction, microstructural evolution, and deformation mechanisms under complex loading conditions. The work integrates experimental investigations with high-fidelity computational modeling approaches—including crystal plasticity finite element modeling (CPFEM) and phase-field (PF) simulations—to uncover the interactions between microstructure, defects, and loading history in determining fatigue performance. Significant contributions include developing predictive frameworks for fatigue life in additive-manufactured alloys by examining the role of defects, anisotropy, and overload effects on crack initiation and propagation. The research further establishes rapid fatigue prediction methodologies using phase-field models to capture microstructural evolution under varied laser scanning strategies, enabling improved process–structure–property relationships. Systematic experimental and simulation studies on 316L stainless steel under multiple overload conditions have provided new insights into cyclic deformation behavior and damage evolution pathways. Active involvement in national research projects has supported the formulation of new computational–experimental strategies for modeling fatigue mechanisms with enhanced accuracy and efficiency. Overall, the research advances the scientific understanding of fatigue behavior in engineered materials and contributes to the development of predictive tools essential for structural reliability, durability assessment, and materials design.

 Profile: Orcid 

Featured Publications

Zhai, H., Wang, Y., & Yang, Y. (2025). Rapid prediction of overload fatigue life based on phase-field modeling of microstructures under different scanning strategies. Additive Manufacturing, 104, 104771. https://doi.org/10.1016/j.addma.2025.104771

Jiang, W., Wu, H., Zhai, H., Wang, Y., Li, D., & Dong, C. (2025). The influence of microstructural features on the fracture performance of specimens fabricated by SLM. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. https://doi.org/10.1177/09544062251336549

Zhai, H., Jiang, W., Wang, Y., Yang, Y., & Lv, H. (2025). Experimental and simulation study on the microstructural evolution and fatigue life of 316L stainless steel under different periodic overload conditions. Engineering Failure Analysis, 109, 109475. https://doi.org/10.1016/j.engfailanal.2025.109475

Sarra Senouci | Mechanical Engineering | Editorial Board Member

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Mrs. Sarra Senouci | Mechanical Engineering
| Editorial Board Member

University of Electronic Science and Technology of China | Algeria

Mrs. Sarra Senouci the research work centers on advanced cryptographic systems, network security, and intelligent detection frameworks, with a strong emphasis on chaotic dynamics, pseudo-random number generation, and secure data transmission. The studies include the development of a novel pseudo-random number generator (PRNG) for fiber optic communication, leveraging nonlinear chaotic behavior to enhance cryptographic strength and improve resistance to prediction attacks. Additional contributions explore a chaotic-based cryptographically secure PRNG designed for high-performance applications requiring strong randomness and low computational overhead. In the domain of cybersecurity, the research introduces deep convolutional neural network architectures for high-precision and real-time DDoS attack detection within software-defined networking environments. This includes models optimized for both feature extraction and rapid classification to mitigate large-scale network threats. Further advancements incorporate feature engineering and ensemble learning techniques to achieve robust, scalable, and resilient DDoS detection frameworks capable of adapting to evolving attack patterns. Earlier academic work includes the design and construction of autonomous sensor networks and the implementation of chaotic systems on FPGA platforms, highlighting strong integration of hardware, communication technologies, and nonlinear system modeling across multiple layers of modern electronic and communication systems.

 Profile:  Google Scholar 

Featured Publications

Senouci, S., Madoune, S. A., Senouci, M. R., Senouci, A., & Tang, Z. (2025). A novel PRNG for fiber optic transmission. Chaos, Solitons & Fractals, 192, 116038. https://doi.org/10.1016/j.chaos.2025.116038

Madoune, S. A., Senouci, S., Dingde, J., & Senouci, A. (2024). Deep convolutional neural network-based high-precision and speed DDOS detection in SDN environments. 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 1–6. https://doi.org/10.1109/iccwamtip64812.2024.10873789

Madoune, S. A., Senouci, S., Setitra, M. A., & Dingde, J. (2024). Toward robust DDOS detection in SDN: Leveraging feature engineering and ensemble learning. 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 1–7. https://doi.org/10.1109/iccwamtip64812.2024.10873648